Journal
AUTOMATICA
Volume 43, Issue 9, Pages 1532-1542Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2007.02.016
Keywords
principal component analysis; dynamics; local approach; fault detection and isolation; covariance structure
Funding
- Engineering and Physical Sciences Research Council [GR/S84354/01] Funding Source: researchfish
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This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. (C) 2007 Elsevier Ltd. All rights reserved.
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